Template-based CCLM / MMLM slope adjustment

Abstract

Systems, methods, and instrumentalities are disclosed for template-based cross component linear model / multimode linear model (CCLM / MMLM) adjustment. In an example, a device, such as a video decoding device or video encoding device, may obtain a prediction model for predicting a coded block. The apparatus may select an adjustment model for adjusting the prediction model from a plurality of adjustment models. The device may adjust the prediction model based on the selected adjustment model. The device may process (e.g., encode and / or decode) a coded block based on the adjusted prediction model.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims the benefit of European Provisional Patent Application No. 22305499.0 filed on April 8, 2022, the contents of which are hereby incorporated herein by reference. Background Art

[0003] Video coding systems may be used to compress digital video signals, for example, to reduce the storage and / or transmission bandwidth required for such signals. Video coding systems may include, for example, block-based, wavelet-based, and / or object-based systems. Summary of the invention

[0004] Systems, methods, and means for prediction model adjustment, such as cross-component linear model / multi-mode linear model (CCLM / MMLM) adjustment, are disclosed. In an example, a video decoder may obtain a prediction model for predicting a coding block. Multiple adjustment models may be used to adjust the prediction model. The video decoder may select an adjustment model from the multiple adjustment models for adjusting the prediction model for the coding block. The video decoder may adjust the prediction model based on the selected adjustment model. The video decoder may decode the coding block based on the adjusted prediction model.

[0005] The video decoder may derive a slope and an offset of the prediction model based on reconstructed samples neighboring the coding block.Adjusting the prediction model may include adjusting the slope and / or offset of the prediction model based on the selected adjustment model.

[0006] The prediction model may include a cross-component prediction model. The video decoder may determine multiple parameters of the cross-component prediction model for predicting the coding block based on adjacent chroma samples and luma samples of the coding block. Different adjustment models may be configured to adjust the parameters of the cross-component prediction model in different ways.

[0007] Different adjustment models can be configured to adjust the prediction model in different ways. For example, an example adjustment model can adjust the slope of the prediction model around a minimum value. An example adjustment model can adjust the slope of the prediction model around a midpoint value. An example adjustment model can adjust the slope of the prediction model around a maximum value. An example adjustment model can adjust the prediction model by adjusting the offset of the prediction model and maintaining the same slope of the prediction model.

[0008] The selected adjustment model may be obtained based on an adjustment model indication in the video data. For example, the device may determine a pivot point of the selected adjustment model based on the adjustment model indication. Adjusting the prediction model based on the selected adjustment model may include: adjusting a slope of the prediction model via the determined pivot point. For example, the device may determine an offset adjustment of the selected adjustment model based on the adjustment model indication. The video decoder may adjust an offset of the prediction model based on the determined offset adjustment while maintaining the same slope of the prediction model.

[0009] The prediction model for predicting the decoding block may include multiple prediction models. The selected adjustment model may include multiple adjustment models configured to independently adjust the prediction model. The video decoder may determine a first pivot point for adjusting the first prediction model and a second pivot point for adjusting the second prediction model based on the selected adjustment model. The video decoder may adjust the slope of the first prediction model via the determined first pivot point, and adjust the slope of the second prediction model via the determined second pivot point. The decoding block may be decoded based on the first adjusted prediction model and the second adjusted prediction model.

[0010] In an example, a video encoder may obtain a prediction model for predicting a coding block. The video encoder may select an adjustment model for adjusting the prediction model from a plurality of adjustment models. The video encoder may adjust the prediction model based on the selected adjustment model. The video encoder may encode the coding block based on the adjusted prediction model.

[0011] The video encoder may obtain a plurality of adjustment models and select a suitable adjustment model for adjusting the prediction model. The video encoder may calculate the difference between the sample value of the decoding block and the predicted sample value predicted using the prediction model adjusted based on the first adjustment model for a first adjustment model among the plurality of adjustment models. The video encoder may calculate the difference between the sample value of the decoding block and the predicted sample value predicted using the prediction model adjusted based on the second adjustment model for a second adjustment model among the plurality of adjustment models. The adjustment model may be selected based at least on comparing the differences. For example, the adjustment model associated with the smallest difference may be selected for the decoding block.

[0012] The prediction model may include a cross-component prediction model. The video encoder may determine multiple parameters of the cross-component prediction model based on adjacent chroma samples and luma samples of the coding block. The first adjustment model and the second adjustment model of the multiple adjustment models can adjust the multiple parameters of the cross-component prediction model in different ways.

[0013] A first adjustment model in the plurality of adjustment models may adjust the slope of the prediction model around a minimum value. A second adjustment model in the plurality of adjustment models may adjust the slope of the prediction model around a midpoint value, and a third adjustment model in the plurality of adjustment models may adjust the slope of the prediction model around a maximum value. The video encoder may test the adjustment models and select an appropriate adjustment model that will produce the best prediction for the coding block.

[0014] The video encoder may adjust the prediction model based on the selected adjustment model by adjusting an offset of the prediction model and maintaining the same slope of the prediction model. The video encoder may determine a pivot point of the selected adjustment model, and adjusting the prediction model based on the selected adjustment model may include adjusting the slope of the prediction model via the determined pivot point. The video encoder may include an adjustment model indicator in the video data, the adjustment model indicator indicating the selected adjustment model and / or the determined pivot point of the selected adjustment model.

[0015] The video encoder may determine an offset adjustment for adjusting the prediction model. The video encoder may adjust the offset of the prediction model based on the determined offset adjustment for the selected adjustment model while maintaining the same slope of the prediction model. The video encoder may include an adjustment model indicator in the video data that indicates the selected adjustment model and / or the determined offset adjustment for the selected adjustment model.

[0016] The prediction model for predicting the decoding block may include multiple linear prediction models, and the selected adjustment model may include multiple adjustment models to independently adjust the prediction model. The video encoder may determine a pivot point for adjusting the first linear prediction model and a different pivot point for adjusting the second linear prediction model. Based on the determination, the video encoder may adjust the slope of the linear prediction model via different pivot points. The video encoder may indicate the selected pivot point via an adjustment model indicator in the video data.

[0017] The systems, methods, and means described herein may relate to decoders. In examples, the systems, methods, and means described herein may relate to encoders. In examples, the systems, methods, and means described herein may relate to signals (e.g., signals from encoders and / or received by decoders). A computer-readable medium may include instructions for causing one or more processors to perform the methods described herein. A computer program product may include instructions that, when executed by one or more processors, cause one or more processors to perform the methods described herein. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1A is a system diagram illustrating an example communication system in which one or more disclosed embodiments may be implemented.

[0019] Figure 1B is an example of a method that can be used according to an embodiment of the present invention. Figure 1A A system diagram of an example wireless transmit / receive unit (WTRU) for use within the illustrated communication system.

[0020] Figure 1C is an example of a method that can be used according to an embodiment of the present invention. Figure 1A System diagram of an example Radio Access Network (RAN) and an example Core Network (CN) for use within the illustrated communication system.

[0021] Figure 1D is an example of a method that can be used according to an embodiment of the present invention. Figure 1A System diagram of yet another example RAN and yet another example CN for use within the illustrated communication system.

[0022] Figure 2 An example video encoder is illustrated.

[0023] Figure 3 An example video decoder is illustrated.

[0024] Figure 4 Examples of systems in which various aspects and examples may be implemented are illustrated.

[0025] Figure 5 An example of the positions of left and upper samples and samples of the current block involved in the CCLM mode is illustrated.

[0026] Fig. 6A and Figure 6B An example of the effect of the slope adjustment parameter is illustrated.

[0027] Figure 7 An example of calculating adjustment values ​​using reconstructed luma and chroma samples is illustrated.

[0028] Figure 8 An example of deriving adjustment values ​​using template samples is illustrated.

[0029] FIG. 9A to FIG. 9B An example of slope modification around an extreme value is illustrated.

[0030] Fig.10 An example of modifying the offset value is illustrated. DETAILED DESCRIPTION

[0031] A more detailed understanding may be obtained from the following description given by way of example in conjunction with the accompanying drawings.

[0032] Figure 1A1 is a diagram illustrating an example communication system 100 in which one or more disclosed embodiments may be implemented. The communication system 100 may be a multiple access system that provides content such as voice, data, video, messaging, broadcast, etc. to multiple wireless users. The communication system 100 may enable multiple wireless users to access such content through sharing of system resources (including wireless bandwidth). For example, the communication system 100 may employ one or more channel access methods such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA), single carrier FDMA (SC-FDMA), zero tail unique word DFT spread OFDM (ZT UW DTS-s OFDM), unique word OFDM (UW-OFDM), resource block filtered OFDM, filter bank multi-carrier (FBMC), etc.

[0033] like Figure 1A As shown, the communication system 100 may include wireless transmit / receive units (WTRUs) 102a, 102b, 102c, 102d, RAN 104 / 113, CN 106 / 115, public switched telephone network (PSTN) 108, Internet 110 and other networks 112, but it should be understood that the disclosed embodiments contemplate any number of WTRUs, base stations, networks, and / or network elements. Each of the WTRUs 102a, 102b, 102c, 102d may be any type of device configured to operate and / or communicate in a wireless environment. By way of example, the WTRUs 102a, 102b, 102c, 102d (any of which may be referred to as a “station” and / or “STA”) may be configured to send and / or receive wireless signals and may include user equipment (UE), a mobile station, a fixed or mobile subscriber unit, a subscription-based unit, a pager, a cellular phone, a personal digital assistant (PDA), a smart phone, a laptop, a netbook, a personal computer, a wireless sensor, a hotspot or Mi-Fi device, an Internet of Things (IoT) device, a watch or other wearable device, a head-mounted display (HMD), a vehicle, a drone, medical devices and applications (e.g., remote surgery), industrial devices and applications (e.g., robots and / or other wireless devices operating in an industrial and / or automated process chain environment), a consumer electronic device, a device operating on a commercial and / or industrial wireless network, etc. Any of the WTRUs 102a, 102b, 102c, and 102d may be interchangeably referred to as a UE.

[0034] The communication system 100 may also include a base station 114a and / or a base station 114b. Each of the base stations 114a, 114b may be any type of device that is configured to wirelessly interface with at least one of the WTRUs 102a, 102b, 102c, 102d to facilitate access to one or more communication networks, such as the CN 106 / 115, the Internet 110, and / or other networks 112. By way of example, the base stations 114a, 114b may be a base transceiver station (BTS), a Node B, an eNode B, a Home Node B, a Home eNode B, a gNB, an NR Node B, a site controller, an access point (AP), a wireless router, and the like. While the base stations 114a, 114b are each depicted as a single element, it will be appreciated that the base stations 114a, 114b may include any number of interconnected base stations and / or network elements.

[0035] The base station 114a may be part of the RAN 104 / 113, which may also include other base stations and / or network elements (not shown), such as a base station controller (BSC), a radio network controller (RNC), a relay node, etc. The base station 114a and / or the base station 114b may be configured to send and / or receive wireless signals on one or more carrier frequencies (which may be referred to as cells (not shown)). These frequencies may be in a licensed spectrum, an unlicensed spectrum, or a combination of licensed and unlicensed spectrums. A cell may provide coverage of wireless services to a specific geographic area, which may be relatively fixed or may change over time. The cell may be further divided into cell sectors. For example, a cell associated with the base station 114a may be divided into three sectors. Therefore, in one embodiment, the base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In an embodiment, the base station 114a may employ multiple-input multiple-output (MIMO) technology and may utilize multiple transceivers for each sector of the cell. For example, beamforming may be used to send and / or receive signals in a desired spatial direction.

[0036] The base stations 114a, 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, 102d over an air interface 116, which may be any suitable wireless communication link (e.g., radio frequency (RF), microwave, centimeter wave, micrometer wave, infrared (IR), ultraviolet (UV), visible light, etc.). The air interface 116 may be established using any suitable radio access technology (RAT).

[0037] More specifically, as noted above, the communication system 100 may be a multiple access system and may employ one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, etc. For example, the base station 114a in the RAN 104 / 113 and the WTRUs 102a, 102b, 102c may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may use Wideband CDMA (WCDMA) to establish the air interface 115 / 116 / 117. WCDMA may include communication protocols such as High Speed ​​Packet Access (HSPA) and / or Evolved HSPA (HSPA+). HSPA may include High Speed ​​Downlink (DL) Packet Access (HSDPA) and / or High Speed ​​UL Packet Access (HSUPA).

[0038] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA) that may establish the air interface 116 using Long Term Evolution (LTE) and / or Advanced LTE (LTE-A) and / or Advanced LTE Pro (LTE-A Pro).

[0039] In one embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement a radio technology such as NR radio access, which may establish the air interface 116 using New Radio (NR).

[0040] In an embodiment, the base station 114a and the WTRUs 102a, 102b, 102c may implement multiple radio access technologies. For example, the base station 114a and the WTRUs 102a, 102b, 102c may together implement LTE radio access and NR radio access, for example using the dual connectivity (DC) principle. Thus, the air interface used by the WTRUs 102a, 102b, 102c may be characterized by multiple types of radio access technologies and / or transmissions transmitted to / from multiple types of base stations (e.g., eNBs and gNBs).

[0041] In other embodiments, the base station 114a and the WTRUs 102a, 102b, 102c may implement radio technologies such as IEEE 802.11 (i.e., Wireless Fidelity (WiFi)), IEEE 802.16 (i.e., Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1X, CDMA2000 EV-DO, Interim Standard 2000 (IS-2000), Interim Standard 95 (IS-95), Interim Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data rates for GSM Evolution (EDGE), GSM EDGE (GERAN), etc.

[0042] Figure 1A The base station 114b in the may be, for example, a wireless router, a Home NodeB, a Home eNodeB, or an access point, and may utilize any suitable RAT to facilitate wireless connectivity in a local area, such as a business location, a home, a vehicle, a campus, an industrial facility, an air corridor (e.g., for use by drones), a road, and the like. In one embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology (such as IEEE 802.11) to establish a wireless local area network (WLAN). In an embodiment, the base station 114b and the WTRUs 102c, 102d may implement a radio technology (such as IEEE 802.15) to establish a wireless personal area network (WPAN). In yet another embodiment, the base station 114b and the WTRUs 102c, 102d may utilize a cellular-based RAT (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, LTE-A Pro, NR, etc.) to establish a picocell or a femtocell. As Figure 1A As shown, base station 114b may have a direct connection to Internet 110. Therefore, base station 114b may not need to access Internet 110 via CN 106 / 115.

[0043] The RAN 104 / 113 may be in communication with the CN 106 / 115, which may be any type of network configured to provide voice, data, applications, and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform advanced security functions, such as user authentication. Although not described in detail in the text, the CN 106 / 115 may be configured to provide voice, data, applications, and / or Voice over Internet Protocol (VoIP) services to one or more of the WTRUs 102a, 102b, 102c, 102d. The data may have different quality of service (QoS) requirements, such as different throughput requirements, latency requirements, error tolerance requirements, reliability requirements, data throughput requirements, mobility requirements, etc. The CN 106 / 115 may provide call control, billing services, mobile location-based services, prepaid calling, Internet connectivity, video distribution, etc., and / or perform advanced security functions, such as user authentication. Figure 1AAlthough not shown in the figure, it will be appreciated that the RAN 104 / 113 and / or the CN 106 / 115 may be in direct or indirect communication with other RANs that employ the same RAT as the RAN 104 / 113 or a different RAT. For example, in addition to being connected to the RAN 104 / 113, which may utilize NR radio technology, the CN 106 / 115 may also be in communication with another RAN (not shown) that employs GSM, UMTS, CDMA 2000, WiMAX, E-UTRA, or WiFi radio technology.

[0044] The CN 106 / 115 may also serve as a gateway for the WTRUs 102a, 102b, 102c, 102d to access the PSTN 108, the Internet 110, and / or other networks 112. The PSTN 108 may include a circuit-switched telephone network that provides plain old telephone service (POTS). The Internet 110 may include a global system of interconnected computer networks and devices that use common communication protocols, such as the Transmission Control Protocol (TCP), the User Datagram Protocol (UDP), and / or the Internet Protocol (IP) in the TCP / IP Internet protocol suite. The networks 112 may include wired communication networks and / or wireless communication networks owned and / or operated by other service providers. For example, the networks 112 may include another CN connected to one or more RANs, which may employ the same RAT as the RAN 104 / 113 or a different RAT.

[0045] Some or all of the WTRUs 102a, 102b, 102c, 102d in the communication system 100 may include multi-mode capabilities (e.g., the WTRUs 102a, 102b, 102c, 102d may include multiple transceivers for communicating with different wireless networks via different wireless links). Figure 1A The illustrated WTRU 102c may be configured to communicate with the base station 114a, which may employ a cellular-based radio technology, and with the base station 114b, which may employ an IEEE 802 radio technology.

[0046] Figure 1B is a system diagram illustrating an example WTRU 102. Figure 1B As shown, the WTRU 102 may include, among other things, a processor 118, a transceiver 120, a transmit / receive element 122, a speaker / microphone 124, a keypad 126, a display / touchpad 128, non-removable memory 130, removable memory 132, a power source 134, a global positioning system (GPS) chipset 136, and / or other peripherals 138. It will be appreciated that the WTRU 102 may include any sub-combination of the foregoing elements while remaining consistent with an embodiment.

[0047] The processor 118 may be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with a DSP core, a controller, a microcontroller, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) circuit, any other type of integrated circuit (IC), a state machine, etc. The processor 118 may perform signal decoding, data processing, power control, input / output processing, and / or any other functionality that enables the WTRU 102 to operate in a wireless environment. The processor 118 may be coupled to the transceiver 120, which may be coupled to the transmit / receive element 122. Although Figure 1B The processor 118 and the transceiver 120 are depicted as separate components, but it is understood that the processor 118 and the transceiver 120 may be integrated together in an electronic package or chip.

[0048] The send / receive element 122 may be configured to send a signal to a base station (e.g., base station 114a) or receive a signal from the base station by an air interface 116. For example, in one embodiment, the send / receive element 122 may be an antenna configured to send and / or receive an RF signal. In an embodiment, the send / receive element 122 may be a transmitter / detector configured to send and / or receive, for example, IR, UV or visible light signals. In another embodiment, the send / receive element 122 may be configured to send and / or receive both RF signals and optical signals. It should be understood that the send / receive element 122 may be configured to send and / or receive any combination of wireless signals.

[0049] Although the transmit / receive element 122 Figure 1B 1 as a single element, the WTRU 102 may include any number of transmit / receive elements 122. More specifically, the WTRU 102 may employ MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit / receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals over the air interface 116.

[0050] The transceiver 120 may be configured to modulate signals to be transmitted by the transmit / receive element 122 and to demodulate signals received by the transmit / receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. For example, the transceiver 120 may include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as NR and IEEE 802.11.

[0051] The processor 118 of the WTRU 102 may be coupled to and may receive user input data from a speaker / microphone 124, a keypad 126, and / or a display / touchpad 128 (e.g., a liquid crystal display (LCD) display unit or an organic light emitting diode (OLED) display unit). The processor 118 may also output user data to the speaker / microphone 124, the keypad 126, and / or the display / touchpad 128. In addition, the processor 118 may access information from and store data in any type of suitable memory, such as a non-removable memory 130 and / or a removable memory 132. The non-removable memory 130 may include a random access memory (RAM), a read-only memory (ROM), a hard disk, or any other type of memory storage device. The removable memory 132 may include a subscriber identity module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 may access information from and store data in a memory that is not physically located on the WTRU 102, such as on a server or a home computer (not shown).

[0052] The processor 118 may receive power from the power source 134, and may be configured to distribute and / or control power to other components in the WTRU 102. The power source 134 may be any suitable device for powering the WTRU 102. For example, the power source 134 may include one or more dry cell batteries (e.g., nickel-cadmium (NiCd), nickel-zinc (NiZn), nickel-metal hydride (NiMH), lithium-ion (Li-ion), etc.), solar cells, fuel cells, etc.

[0053] The processor 118 may also be coupled to the GPS chipset 136, which may be configured to provide location information (e.g., longitude and latitude) regarding the current location of the WTRU 102. In addition to or in lieu of the information from the GPS chipset 136, the WTRU 102 may receive location information from a base station (e.g., base stations 114a, 114b) over the air interface 116 and / or determine its location based on the timing of signals received from two or more nearby base stations. It will be appreciated that the WTRU 102 may acquire location information by any suitable location-determination method while remaining consistent with an embodiment.

[0054] The processor 118 may also be coupled to other peripherals 138, which may include one or more software modules and / or hardware modules that provide additional features, functionality, and / or wired or wireless connectivity. For example, the peripherals 138 may include an accelerometer, an electronic compass, a satellite transceiver, a digital camera (for photos and / or video), a universal serial bus (USB) port, a vibration device, a television transceiver, a hands-free headset, module, a frequency modulation (FM) radio unit, a digital music player, a media player, a video game player module, an Internet browser, a virtual reality and / or augmented reality (VR / AR) device, an activity tracker, etc. Peripheral device 138 may include one or more sensors, which may be one or more of the following: a gyroscope, an accelerometer, a Hall effect sensor, a magnetometer, an orientation sensor, a proximity sensor, a temperature sensor, a time sensor; a geolocation sensor; an altimeter, a light sensor, a touch sensor, a magnetometer, a barometer, a gesture sensor, a biometric sensor, and / or a humidity sensor.

[0055] The WTRU 102 may include a full-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for UL (e.g., for transmission) and downlink (e.g., for reception)) may be concurrent and / or simultaneous. The full-duplex radio may include an interference management unit for reducing and / or substantially eliminating self-interference via signal processing performed via hardware (e.g., a choke) or via a processor (e.g., a separate processor (not shown) or via the processor 118). In an embodiment, the WRTU 102 may include a half-duplex radio for which transmission and reception of some or all signals (e.g., associated with specific subframes for UL (e.g., for transmission) or downlink (e.g., for reception)) may be concurrent and / or simultaneous.

[0056] Figure 1C 1 is a system diagram illustrating the RAN 104 and the CN 106 in accordance with an embodiment. As noted above, the RAN 104 may employ an E-UTRA radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 104 may also be in communication with the CN 106.

[0057] The RAN 104 may include evolved Node-Bs 160a, 160b, 160c, though it will be appreciated that the RAN 104 may include any number of evolved Node-Bs while remaining consistent with an embodiment. The evolved Node-Bs 160a, 160b, 160c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the evolved Node-Bs 160a, 160b, 160c may implement MIMO technology. Thus, the evolved Node-B 160a, for example, may use multiple antennas to transmit wireless signals to and / or receive wireless signals from the WTRU 102a.

[0058] Each of the evolved Node Bs 160a, 160b, 160c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in UL and / or DL, etc. Figure 1C As shown, the eNode-Bs 160a, 160b, 160c may communicate with one another via an X2 interface.

[0059] Figure 1C The illustrated CN 106 may include a mobility management entity (MME) 162, a serving gateway (SGW) 164, and a packet data network (PDN) gateway (or PGW) 166. While each of the foregoing elements is depicted as being part of the CN 106, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0060] The MME 162 may be connected to each of the evolved Node-Bs 162a, 162b, 162c in the RAN 104 via an S1 interface and may serve as a control node. For example, the MME 162 may be responsible for authenticating users of the WTRUs 102a, 102b, 102c, bearer activation / deactivation, selecting a particular serving gateway during an initial attach of the WTRUs 102a, 102b, 102c, etc. The MME 162 may provide a control plane function for switching between the RAN 104 and other RANs (not shown) that employ other radio technologies, such as GSM and / or WCDMA.

[0061] The SGW 164 may be connected to each of the evolved Node-Bs 160a, 160b, 160c in the RAN 104 via an S1 interface. The SGW 164 may generally route and forward user data packets to / from the WTRUs 102a, 102b, 102c. The SGW 164 may perform other functions, such as anchoring the user plane during inter-evolved Node-B handovers, triggering paging when DL data is available for the WTRUs 102a, 102b, 102c, managing and storing the context of the WTRUs 102a, 102b, 102c, and the like.

[0062] The SGW 164 may be connected to the PGW 166, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices.

[0063] The CN 106 may facilitate communications with other networks. For example, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to circuit-switched networks, such as the PSTN 108, to facilitate communications between the WTRUs 102a, 102b, 102c and traditional land-line communications devices. For example, the CN 106 may include, or may be in communication with, an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 106 and the PSTN 108. In addition, the CN 106 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers.

[0064] Although the WTRU Figures 1A to 1D Although described as wireless terminals, it is contemplated that in certain representative embodiments, such terminals may (eg, temporarily or permanently) use a wired communication interface with a communication network.

[0065] In a representative embodiment, the other network 112 may be a WLAN.

[0066] A WLAN in infrastructure basic service set (BSS) mode may have an access point (AP) for the BSS and one or more stations (STA) associated with the AP. The AP may have access or an interface to a distribution system (DS) or another type of wired / wireless network that carries traffic to and / or carries traffic away from the BSS. Traffic originating from outside the BSS and leading to the STA may be reached by the AP and may be delivered to the STA. Traffic originating from the STA and leading to a destination outside the BSS may be transmitted to the AP to be delivered to the corresponding destination. Traffic between STAs within the BSS may be transmitted by the AP, for example, wherein the source STA may transmit traffic to the AP, and the AP may deliver traffic to the destination STA. Traffic between STAs within the BSS may be considered and / or referred to as point-to-point traffic. Point-to-point traffic may be transmitted between the source STA and the destination STA (e.g., directly between them) using a direct link establishment (DLS). In certain representative embodiments, the DLS may use 802.11e DLS or 802.11z tunnel DLS (TDLS). A WLAN using an independent BSS (IBSS) mode may not have an AP, and STAs within or using the IBSS (eg, all STAs in the STA) may communicate directly with each other. The IBSS communication mode may sometimes be referred to herein as an "ad hoc" communication mode.

[0067] When using the 802.11ac infrastructure operating mode or a similar operating mode, the AP may send beacons on a fixed channel (such as a primary channel). The primary channel may be a fixed width (e.g., a 20 MHz wide bandwidth) or a width dynamically set via signaling. The primary channel may be an operating channel of the BSS and may be used by the STA to establish a connection with the AP. In certain representative embodiments, carrier sense multiple access / collision avoidance (CSMA / CA) may be implemented, for example, in an 802.11 system. For CSMA / CA, a STA (e.g., each STA) (including the AP) may listen to the primary channel. In the case where the primary channel is listened / detected and / or determined to be busy by a specific STA, the specific STA may back off. One STA (e.g., only one station) may transmit at any given time in a given BSS.

[0068] High throughput (HT) STAs may communicate using a 40 MHz wide channel, for example, via a combination of a primary 20 MHz channel and adjacent or non-adjacent 20 MHz channels to form a 40 MHz wide channel.

[0069] Very high throughput (VHT) STA can support 20MHz, 40MHz, 80MHz and / or 160MHz wide channels. 40MHz channels and / or 80MHz channels can be formed by combining continuous 20MHz channels. 160MHz channels can be formed by combining 8 continuous 20MHz channels, or by combining two non-continuous 80MHz channels (this can be called 80+80 configuration). For 80+80 configuration, after channel coding, the data can pass through a segment parser that can divide the data into two streams. Each stream can be processed by inverse fast Fourier transform (IFFT) and time domain processing separately. These streams can be mapped to two 80MHz channels, and data can be sent by the transmitter STA. At the receiver of the receiver STA, the above-mentioned operation for the 80+80 configuration can be reversed, and the combined data can be transmitted to the medium access control (MAC).

[0070] 802.11af and 802.11ah support operating modes below 1GHz. Channel operating bandwidths and carriers are reduced in 802.11af and 802.11ah relative to those used in 802.11n and 802.11ac. 802.11af supports 5MHz, 10MHz, and 20MHz bandwidths in the TV White Space (TVWS) spectrum, and 802.11ah supports 1MHz, 2MHz, 4MHz, 8MHz, and 16MHz bandwidths using non-TVWS spectrum. According to a representative embodiment, 802.11ah may support instrument type control / machine type communications, such as MTC devices in macro coverage areas. MTC devices may have certain capabilities, such as limited capabilities, including support for (e.g., only support for) certain bandwidths and / or limited bandwidths. MTC devices may include batteries with battery life above a threshold (e.g., to maintain very long battery life).

[0071] WLAN systems that can support multiple channels and channel bandwidths (such as 802.11n, 802.11ac, 802.11af, and 802.11ah) include channels that can be designated as primary channels. The primary channel may have a bandwidth equal to the maximum common operating bandwidth supported by all STAs in the BSS. The bandwidth of the primary channel may be set and / or limited by a STA (which supports the minimum bandwidth operating mode) from all STAs operating in the BSS. In the example of 802.11ah, for STAs (e.g., MTC-type devices) that support (e.g., only support) a 1MHz mode, the primary channel may be 1MHz wide, even if the AP and other STAs in the BSS support 2MHz, 4MHz, 8MHz, 16MHz, and / or other channel bandwidth operating modes. Carrier sensing and / or network allocation vector (NAV) settings may depend on the state of the primary channel. If the primary channel is busy, for example, because a STA (supporting only a 1MHz operating mode) is sending to the AP, the entire available frequency band may be considered busy even if most of the frequency bands remain idle and may be available.

[0072] In the United States, the available frequency band for 802.11ah is 902MHz to 928MHz. In South Korea, the available frequency band is 917.5MHz to 923.5MHz. In Japan, the available frequency band is 916.5MHz to 927.5MHz. The total bandwidth available for 802.11ah is 6MHz to 26MHz, depending on the country code.

[0073] Figure 1D 1 is a system diagram illustrating the RAN 113 and the CN 115 in accordance with an embodiment. As noted above, the RAN 113 may employ NR radio technology to communicate with the WTRUs 102a, 102b, 102c over the air interface 116. The RAN 113 may also be in communication with the CN 115.

[0074] The RAN 113 may include gNBs 180a, 180b, 180c, though it will be appreciated that the RAN 113 may include any number of gNBs while remaining consistent with an embodiment. The gNBs 180a, 180b, 180c may each include one or more transceivers for communicating with the WTRUs 102a, 102b, 102c over the air interface 116. In one embodiment, the gNBs 180a, 180b, 180c may implement MIMO technology. For example, the gNBs 180a, 180b may utilize beamforming to send signals to and / or receive signals from the gNBs 180a, 180b, 180c. Thus, the gNB 180a may, for example, use multiple antennas to send wireless signals to and / or receive wireless signals from the WTRU 102a. In an embodiment, the gNBs 180a, 180b, 180c may implement carrier aggregation technology. For example, gNB 180a may transmit multiple component carriers to WTRU 102a (not shown). A subset of these component carriers may be on unlicensed spectrum, while the remaining component carriers may be on licensed spectrum. In an embodiment, gNBs 180a, 180b, 180c may implement coordinated multi-point (CoMP) technology. For example, WTRU 102a may receive coordinated transmissions from gNB 180a and gNB 180b (and / or gNB 180c).

[0075] The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using transmissions associated with parameter sets that may be scalable. For example, OFDM symbol spacing and / or OFDM subcarrier spacing may vary for different transmissions, different cells, and / or different portions of the wireless transmission spectrum. The WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using subframes or Transmission Time Intervals (TTIs) of varying or scalable lengths (e.g., containing varying numbers of OFDM symbols and / or varying absolute time lengths over time).

[0076] The gNBs 180a, 180b, 180c may be configured to communicate with the WTRUs 102a, 102b, 102c in a standalone configuration and / or a non-standalone configuration. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c while not accessing other RANs (e.g., such as the eNodeBs 160a, 160b, 160c). In a standalone configuration, the WTRUs 102a, 102b, 102c may use one or more of the gNBs 180a, 180b, 180c as mobility anchors. In a standalone configuration, the WTRUs 102a, 102b, 102c may communicate with the gNBs 180a, 180b, 180c using signals in an unlicensed band. In a non-standalone configuration, the WTRUs 102a, 102b, 102c may communicate / connect with the gNB 180a, 180b, 180c while also communicating / connecting with another RAN, such as the eNode-B 160a, 160b, 160c. For example, the WTRUs 102a, 102b, 102c may implement the DC principle to communicate with one or more gNBs 180a, 180b, 180c and one or more eNode-Bs 160a, 160b, 160c substantially simultaneously. In a non-standalone configuration, the eNode-B 160a, 160b, 160c may act as a mobility anchor for the WTRUs 102a, 102b, 102c, and the gNB 180a, 180b, 180c may provide additional coverage and / or throughput for serving the WTRUs 102a, 102b, 102c.

[0077] Each of the gNBs 180a, 180b, 180c may be associated with a particular cell (not shown) and may be configured to handle radio resource management decisions, handover decisions, scheduling of users in UL and / or DL, support of network slicing, dual connectivity, interworking between NR and E-UTRA, routing of user plane data towards a user plane function (UPF) 184a, 184b, routing of control plane information towards an access and mobility management function (AMF) 182a, 182b, etc. Figure 1D As shown, gNBs 180a, 180b, and 180c may communicate with each other via an Xn interface.

[0078] Figure 1DThe illustrated CN 115 may include at least one AMF 182a, 182b, at least one UPF 184a, 184b, at least one session management function (SMF) 183a, 183b, and possible data networks (DNs) 185a, 185b. Although each of the foregoing elements is depicted as part of the CN 115, it should be understood that any of these elements may be owned and / or operated by an entity other than the CN operator.

[0079] The AMF 182a, 182b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N2 interface and may act as a control node. For example, the AMF 182a, 182b may be responsible for authenticating users of the WTRU 102a, 102b, 102c, support of network slicing (e.g., handling of different PDU sessions with different requirements), selecting a specific SMF 183a, 183b, management of registration areas, termination of NAS signaling, mobility management, etc. The AMF 182a, 182b may use network slicing to customize CN support for the WTRU 102a, 102b, 102c based on the type of services utilized by the WTRU 102a, 102b, 102c. For example, different network slices may be established for different use cases, such as services relying on ultra-reliable low latency (URLLC) access, services relying on enhanced mobile broadband (eMBB) access, services for machine type communication (MTC) access, etc. The AMF 162 may provide a control plane function for switching between the RAN 113 and other RANs (not shown) that employ other radio technologies, such as LTE, LTE-A, LTE-A Pro, and / or non-3GPP access technologies, such as WiFi.

[0080] The SMF 183a, 183b may be connected to the AMF 182a, 182b in the CN 115 via the N11 interface. The SMF 183a, 183b may also be connected to the UPF 184a, 184b in the CN 115 via the N4 interface. The SMF 183a, 183b may select and control the UPF 184a, 184b, and configure the traffic routing through the UPF 184a, 184b. The SMF 183a, 183b may perform other functions, such as managing and allocating UE IP addresses, managing PDU sessions, controlling policy enforcement and QoS, providing downlink data notification, etc. The PDU session type may be IP-based, non-IP-based, Ethernet-based, etc.

[0081] The UPF 184a, 184b may be connected to one or more of the gNBs 180a, 180b, 180c in the RAN 113 via the N3 interface, which may provide the WTRUs 102a, 102b, 102c with access to packet-switched networks, such as the Internet 110, to facilitate communications between the WTRUs 102a, 102b, 102c and IP-enabled devices. The UPF 184, 184b may perform other functions, such as routing and forwarding packets, enforcing user plane policies, supporting multi-homed PDU sessions, handling user plane QoS, buffering downlink packets, providing mobility anchoring, etc.

[0082] The CN 115 may facilitate communications with other networks. For example, the CN 115 may include or may communicate with an IP gateway (e.g., an IP Multimedia Subsystem (IMS) server) that acts as an interface between the CN 115 and the PSTN 108. In addition, the CN 115 may provide the WTRUs 102a, 102b, 102c with access to other networks 112, which may include other wired networks and / or wireless networks owned and / or operated by other service providers. In one embodiment, the WTRUs 102a, 102b, 102c may be connected to local data networks (DNs) 185a, 185b via the UPF 184a, 184b via the N3 interface to the UPF 184a, 184b and the N6 interface between the UPF 184a, 184b and the DN 185a, 185b.

[0083] Given that Figures 1A to 1D as well as Figures 1A to 1D Corresponding to the description of the present invention, one or more or all of the functions described herein with reference to one or more of the following may be performed by one or more simulation devices (not shown): WTRU102a-d, base station 114a-b, evolved Node B 160a-c, MME 162, SGW 164, PGW 166, gNB 180a-c, AMF182a-b, UPF 184a-b, SMF 183a-b, DN 185a-b and / or any other device described herein. The simulation device may be one or more devices configured to emulate one or more or all of the functions described herein. For example, the simulation device may be used to test other devices and / or simulate network and / or WTRU functions.

[0084] The simulation device may be designed to implement one or more tests of other devices in a laboratory environment and / or in an operator network environment. For example, one or more simulation devices may perform one or more functions or all functions while being fully or partially implemented and / or deployed as part of a wired and / or wireless communication network in order to test other devices within the communication network. One or more simulation devices may perform one or more functions or all functions while being temporarily implemented / deployed as part of a wired and / or wireless communication network. The simulation device may be directly coupled to another device for testing purposes and / or may use over-the-air wireless communications to perform testing.

[0085] One or more simulation devices can perform one or more (including all) functions without being implemented / deployed as part of a wired and / or wireless communication network. For example, the simulation device can be used in a test scenario in a test laboratory and / or a non-deployed (e.g., testing) wired and / or wireless communication network to implement testing of one or more components. One or more simulation devices can be test equipment. Direct RF coupling and / or wireless communication via RF circuitry (e.g., which can include one or more antennas) can be used by the simulation device to send and / or receive data.

[0086] The present application describes a number of aspects, including tools, features, examples, models, methods, etc. Many of these aspects are described in a particular manner, and at least in order to illustrate individual features, are usually described in a manner that may sound restrictive. However, this is to describe clearly and does not limit the application or scope of these aspects. In fact, all different aspects can be combined and interchanged to provide further aspects. In addition, these aspects can also be combined and interchanged with the aspects described in the earlier submission.

[0087] The aspects described and contemplated in this application can be implemented in many different forms. Figures 5 to 10 Examples may be provided, but other examples are also contemplated. Figures 5 to 10 The discussion of the present invention does not limit the breadth of specific implementations. At least one of these aspects generally relates to video encoding and decoding, and at least one other aspect generally relates to sending a generated or encoded bitstream. These and other aspects can be implemented as methods, apparatus, a computer-readable storage medium having stored thereon instructions for encoding or decoding video data according to any of the methods, and / or a computer-readable storage medium having stored thereon a bitstream generated according to any of the methods.

[0088] In this application, the terms "reconstruction" and "decoding" may be used interchangeably, the terms "pixel" and "sample" may be used interchangeably, and the terms "image", "picture" and "frame" may be used interchangeably.

[0089] Various methods are described herein, and each method includes one or more steps or actions for implementing the method. Unless the correct operation method requires a specific order of steps or actions, the order and / or purpose of specific steps and / or actions can be modified or combined. In addition, in various examples, terms such as "first", "second" and the like can be used to modify elements, parts, steps, operations, etc., such as "first decoding" and "second decoding". Unless specifically required, the use of such terms does not imply the ordering of the modification operation. Therefore, in this example, the first decoding does not need to be performed before the second decoding, and can, for example, occur before, during, or in an overlapping time period of the second decoding.

[0090] like Figure 2 and Figure 3 As shown, various methods and other aspects described in this application can be used to modify modules (e.g., decoding modules) of video encoder 200 and decoder 300. In addition, the subject matter disclosed herein can be applied to, for example, any type, format, or version of video decoding (whether described in a standard or described in a recommendation), whether pre-existing or developed in the future, as well as extensions of any such standards and recommendations. Unless otherwise indicated or technically excluded, the aspects described in this application can be used alone or in combination.

[0091] Various numerical values ​​are used in the examples described in this application, such as in relation to FIG. Fig.10 , equation (5), numerical values ​​cited in the examples shown and discussed in Table 1, the number of intra modes, the downsampling ratio, the number of adjustment values, etc. These and other specific values ​​are for the purpose of describing the examples, and the described aspects are not limited to these specific values.

[0092] Figure 2 is a diagram illustrating an example video encoder. Variations of the example encoder 200 are contemplated, but the following describes encoder 200 for clarity without describing all contemplated variations.

[0093] Before being encoded, the video sequence may undergo a pre-encoding process (201), for example, applying a color transform to an input color picture (e.g., conversion from RGB 4:4:4 to YCbCr 4:2:0), or performing a remapping of input picture components to obtain a signal distribution that is more resilient to compression (e.g., using histogram equalization of one of the color components). Metadata may be associated with the pre-processing and appended to the bitstream.

[0094] In encoder 200, a picture is encoded by encoder elements as described below. The picture to be encoded is partitioned (202) and processed in units such as coding units (CUs). Each unit is encoded, for example, using intra mode or inter mode. When a unit is encoded in intra mode, the unit performs intra prediction (260). In inter mode, motion estimation (275) and compensation (270) are performed. The encoder decides (205) which of intra mode or inter mode to use to encode the unit, and indicates the intra / inter decision by, for example, a prediction mode flag. The prediction residual is calculated, for example, by subtracting (210) the predicted block from the original image block.

[0095] The prediction residual is then transformed (225) and quantized (230). The quantized transform coefficients, along with motion vectors and other syntax elements such as picture partition information, are entropy encoded (245) to output a bitstream. The encoder may skip the transform and apply quantization directly to the untransformed residual signal. The encoder may bypass both the transform and quantization, i.e., directly code the residual without applying the transform or quantization process.

[0096] The encoder decodes the coded block to provide a reference for further prediction. The quantized transform coefficients are dequantized (240) and inverse transformed (250) to decode the prediction residual. The image block is reconstructed by combining the decoded prediction residual and the predicted block (255). An in-loop filter (265) is applied to the reconstructed picture to perform, for example, deblocking / SAO (sample adaptive offset) / ALF (adaptive loop filter) filtering to reduce coding artifacts. The filtered image is stored at a reference picture buffer (280).

[0097] Figure 3 is a diagram illustrating an example of a video decoder. In the example decoder 300, a bitstream is decoded by decoder elements as described below. The video decoder 300 generally performs the same Figure 2 The decoding process is the reverse of the encoding process described in . The encoder 200 also typically performs video decoding as part of encoding the video data.

[0098] Specifically, the input to the decoder includes a video bitstream, which may be generated by the video encoder 200. The bitstream is first entropy decoded (330) to obtain transform coefficients, prediction modes, motion vectors, and / or other decoded information. Picture partition information indicates how the picture is partitioned. Thus, the decoder may partition (335) the picture according to the decoded picture partition information. The transform coefficients are dequantized (340) and inverse transformed (350) to decode the prediction residual. The image block is reconstructed by combining (355) the decoded prediction residual and the predicted block. The predicted block may be obtained (370) from intra-frame prediction (360) or motion compensated prediction (i.e., inter-frame prediction) (375). An in-loop filter (365) is applied to the reconstructed image. The filtered image is stored at a reference picture buffer (380). For example, for a given picture, the contents of the reference picture buffer 380 on the decoder 300 side may be the same as the contents of the reference picture buffer 280 on the encoder 200 side (e.g., for the same picture).

[0099] The decoded picture may also undergo post-decoding processing (385), such as an inverse color transform (e.g., conversion from YCbCr 4:2:0 to RGB 4:4:4) or performing an inverse remapping that is the inverse of the remapping process performed in the pre-encoding process (201). The post-decoding processing may use metadata derived in the pre-encoding process and signaled in the bitstream. In an example, the decoded image (e.g., after applying the in-loop filter (365) and / or after the post-decoding processing (385), if post-decoding processing is used) may be transmitted to a display device for presentation to a user.

[0100] Figure 4 4 is a diagram showing an example of a system in which various aspects and examples described herein can be implemented. System 400 may be embodied as a device including various components described below and configured to perform one or more aspects of the aspects described in this document. Examples of such devices include, but are not limited to, various electronic devices such as personal computers, laptop computers, smart phones, tablet computers, digital multimedia set-top boxes, digital television receivers, personal video recording systems, connected home appliances, and servers. The elements of system 400 may be embodied in a single integrated circuit (IC), multiple ICs, and / or discrete components, either individually or in combination. For example, in at least one example, the processing and encoder / decoder elements of system 400 are distributed over multiple ICs and / or discrete components. In various examples, system 400 is communicatively coupled to one or more other systems or other electronic devices via, for example, a communication bus or through dedicated input ports and / or output ports. In various examples, system 400 is configured to implement one or more aspects of the aspects described in this document.

[0101] The system 400 includes at least one processor 410 configured to execute instructions loaded therein for implementing, for example, various aspects described in this document. The processor 410 may include embedded memory, input-output interfaces, and various other circuits as known in the art. The system 400 includes at least one memory 420 (e.g., a volatile memory device and / or a non-volatile memory device). The system 400 includes a storage device 440, which may include a non-volatile memory and / or a volatile memory, including but not limited to an electrically erasable programmable read-only memory (EEPROM), a read-only memory (ROM), a programmable read-only memory (PROM), a random access memory (RAM), a dynamic random access memory (DRAM), a static random access memory (SRAM), a flash memory, a magnetic disk drive, and / or an optical disk drive. As non-limiting examples, the storage device 440 may include an internal storage device, an attached storage device (including a removable and non-removable storage device), and / or a network-accessible storage device.

[0102] The system 400 includes an encoder / decoder module 430, which is configured to process data, for example, to provide encoded video or decoded video, and the encoder / decoder module 430 may include its own processor and memory. The encoder / decoder module 430 represents a module that may be included in a device to perform encoding and / or decoding functions. As is well known, a device may include one or both of an encoding module and a decoding module. Additionally, the encoder / decoder module 430 may be implemented as a separate element of the system 400, or may be incorporated into the processor 410 as a combination of hardware and software known to those skilled in the art.

[0103] Program code to be loaded onto the processor 410 or the encoder / decoder 430 to perform various aspects described in this document may be stored in the storage device 440 and subsequently loaded onto the memory 420 for execution by the processor 410. According to various examples, one or more of the processor 410, the memory 420, the storage device 440, and the encoder / decoder module 430 may store one or more of various items during the execution of the processes described in this document. Such stored items may include, but are not limited to, input video, decoded video or partially decoded video, bitstreams, matrices, variables, and intermediate or final results of processing equations, formulas, operations, and operation logic.

[0104] In an example, memory inside the processor 410 and / or the encoder / decoder module 430 is used to store instructions and provide working memory for processing required during encoding or decoding. However, in other examples, memory external to the processing device (e.g., the processing device may be the processor 410 or the encoder / decoder module 430) is used for one or more of these functions. The external memory may be a memory 420 and / or a storage device 440, such as a dynamic volatile memory and / or a non-volatile flash memory. In several examples, the external non-volatile flash memory is used to store, for example, an operating system for a television. In at least one example, a fast external dynamic volatile memory (such as RAM) is used as working memory for video encoding and decoding operations.

[0105] Inputs to the elements of system 400 may be provided through various input devices as indicated in block 445. Such input devices include, but are not limited to: (i) a radio frequency (RF) section that receives an RF signal, such as that transmitted over the air by a broadcaster; (ii) a component (COMP) input terminal (or a set of COMP input terminals); (iii) a universal serial bus (USB) input terminal; and / or (iv) a high-definition multimedia interface (HDMI) input terminal. Other examples ( Figure 4 ) includes composite video.

[0106] In various examples, the input device of frame 445 has the corresponding input processing element associated as known in the art.For example, the RF part can be associated with the element suitable for the following: (i) select the required frequency (also referred to as selecting signal, or limiting the signal band to a band), (ii) down-convert the selected signal, (iii) again band-limit to a narrower band to select the signal band that (for example) can be called a channel in some examples, (iv) demodulate the signal through down-conversion and band-limited, (v) perform error correction, and / or (vi) demultiplex to select the required data packet stream. The RF part of various examples includes one or more elements for performing these functions, such as frequency selector, signal selector, band limiter, channel selector, filter, down-converter, demodulator, error corrector and demultiplexer. The RF part can include a tuner that performs various functions in these functions, including, for example, down-converting the received signal to a lower frequency (for example, intermediate frequency or near baseband frequency) or to baseband. In a set-top box example, the RF part and its associated input processing element receive the RF signal sent by wired (for example, cable) medium, and filter to the desired frequency band by filtering, down-conversion and again to perform frequency selection.Various examples rearrange the order of above-mentioned (and other) elements, remove some elements in these elements, and / or add other elements that perform similar or different functions.Adding element can include inserting element between existing elements, for example, inserting amplifier and analog-to-digital converter.In various examples, the RF part includes antenna.

[0107] The USB and / or HDMI terminals may include corresponding interface processors for connecting the system 400 to other electronic devices across the USB and / or HDMI connections. It should be understood that various aspects of input processing (e.g., Reed-Solomon error correction) may be implemented as needed, for example, in a separate input processing IC or in the processor 410. Similarly, various aspects of USB or HDMI interface processing may be implemented as needed in a separate interface IC or in the processor 410. The demodulated, error-corrected, and demultiplexed streams are provided to various processing elements, including, for example, the processor 410 and the encoder / decoder 430, which operate in conjunction with the memory and storage elements to process the data stream as needed for presentation on the output device.

[0108] The various components of system 400 may be disposed within an integrated housing. Within the integrated housing, the various components may be interconnected and transmit data between the components using a suitable connection arrangement 425 (e.g., an internal bus known in the art, including an inter-chip (I2C) bus, wiring, and a printed circuit board).

[0109] System 400 includes a communication interface 450 that enables communication with other devices via a communication channel 460. Communication interface 450 may include, but is not limited to, a transceiver configured to send and receive data through communication channel 460. Communication interface 450 may include, but is not limited to, a modem or a network card, and communication channel 460 may be implemented, for example, within a wired and / or wireless medium.

[0110] In various examples, data is streamed or otherwise provided to the system 400 using a wireless network, such as a Wi-Fi network, e.g., IEEE 802.11 (IEEE refers to the Institute of Electrical and Electronics Engineers). The Wi-Fi signals of these examples are received via a communication channel 460 and a communication interface 450 suitable for Wi-Fi communications. The communication channel 460 of these examples is typically connected to an access point or router that provides access to external networks, including the Internet, to allow streaming applications and other cross-top communications. Other examples provide streaming data to the system 400 using a set-top box that delivers data via an HDMI connection of an input box 445. Still other examples use an RF connection of an input box 445 to provide streaming data to the system 400. As described above, various examples provide data in a non-streaming manner. Additionally, various examples use wireless networks other than Wi-Fi, such as cellular networks or network.

[0111] The system 400 may provide output signals to various output devices, including a display 475, a speaker 485, and other peripherals 495. The display 475 of various examples includes, for example, one or more of a touch screen display, an organic light emitting diode (OLED) display, a curved display, and / or a foldable display. The display 475 may be used for a television, a tablet computer, a laptop, a cellular phone (mobile phone), or other devices. The display 475 may also be integrated with other components (e.g., as in a smart phone), or may be independent (e.g., an external monitor for a laptop computer). In various examples, other peripherals 495 include one or more of an independent digital video disc (or digital versatile disc) (DVD, for both terms), a disc player, a stereo system, and / or a lighting system. Various examples use one or more peripherals 495 that provide functions based on the output of the system 400. For example, a disc player performs the function of playing the output of the system 400.

[0112] In various examples, control signals are transmitted between the system 400 and the display 475, speaker 485, or other peripheral device 495 using signaling such as AV.Link, consumer electronics control (CEC), or other communication protocols that enable device-to-device control with or without user intervention. Output devices may be communicatively coupled to the system 400 via dedicated connections through respective interfaces 470, 480, and 490. Alternatively, the output devices may be connected to the system 400 via a communication interface 450 using a communication channel 460. The display 475 and speaker 485 may be integrated into a single unit with other components of the system 400 in an electronic device such as, for example, a television. In various examples, the display interface 470 includes a display driver, such as, for example, a timing controller (TCon) chip.

[0113] For example, if the RF part of input 445 is a part of a separate set-top box, then display 475 and speaker 485 may alternatively be separated from one or more components in other components. In various examples where display 475 and speaker 485 are external components, output signals may be provided via a dedicated output connection (including, for example, HDMI port, USB port or COMP output). These examples may be performed by computer software implemented by processor 410 or by hardware or by a combination of hardware and software. As a non-limiting example, these examples may be implemented by one or more integrated circuits. Memory 420 may be any type suitable for a technical environment, and may be implemented using any appropriate data storage technology, as a non-limiting example, such as an optical memory device, a magnetic memory device, a semiconductor-based memory device, a fixed memory and a removable memory. Processor 410 may be any type suitable for a technical environment, and as a non-limiting example, may include one or more of a microprocessor, a general-purpose computer, a special-purpose computer and a processor based on a multi-core architecture.

[0114] Various implementations involve decoding. As used in this application, "decoding" may encompass, for example, all or part of a process performed on a received coded sequence to produce a final output suitable for display. In various examples, such processes include one or more of the processes typically performed by a decoder, such as entropy decoding, inverse quantization, inverse transform, and differential decoding. In various examples, such processes also include or alternatively include processes performed by decoders of various implementations described in this application, such as: obtaining a prediction model for predicting a decoding block; selecting an adjustment model for adjusting the prediction model from a plurality of adjustment models; adjusting the prediction model based on the selected adjustment model; and decoding the decoding block based on the adjusted prediction model, etc.

[0115] As another example, in one example, "decoding" refers only to entropy decoding, in another example, "decoding" refers only to differential decoding, and in another example, "decoding" refers to a combination of entropy decoding and differential decoding. Whether the phrase "decoding process" is intended to refer specifically to a subset of operations or broadly to a broader decoding process will be clear based on the context of the specific description and is considered to be well understood by those skilled in the art.

[0116] Various implementations involve encoding. In a manner similar to the discussion above about "decoding", "encoding" as used in this application may cover, for example, all or part of the processes performed on an input video sequence to produce an encoded bitstream. In various examples, such processes include one or more of the processes typically performed by an encoder, such as partitioning, differential encoding, transforms, quantization, and entropy encoding. In various examples, such processes also include or alternatively include processes performed by encoders of various implementations described in this application, such as: obtaining a prediction model for predicting a decoding block; selecting an adjustment model for adjusting the prediction model from a plurality of adjustment models; adjusting the prediction model based on the selected adjustment model; and encoding the decoding block based on the adjusted prediction model, etc.

[0117] As another example, in one example, "encoding" refers only to entropy encoding, in another example, "encoding" refers only to differential encoding, and in another example, "encoding" refers to a combination of differential encoding and entropy encoding. Whether the phrase "encoding process" specifically refers to a subset of operations or broadly refers to a broader encoding process will be clear based on the context of the specific description and is believed to be well understood by those skilled in the art.

[0118] Note that syntax elements as used herein are descriptive terms, such as coding syntax indicating that an indication of a pivot point is to be determined, an adjustment model indicator indicating that an adjustment model is to be used to adjust a prediction model, and / or a first pivot point indicator and a second pivot point indicator indicating that a first pivot point and a second pivot point for adjusting a prediction model associated with a coding block are to be determined, respectively, etc. Therefore, they do not exclude the use of other syntax element names or functions.

[0119] When the figures are presented as flow charts, it should be understood that they also provide block diagrams of corresponding devices. Similarly, when the figures are presented as block diagrams, it should be understood that they also provide flow charts of corresponding methods / processes.

[0120] The specific implementations and aspects described herein may be implemented in, for example, a method or process, a device, a software program, a data stream, or a signal. Even if only discussed in the context of a single form of specific implementation (e.g., discussed only as a method), the specific implementation of the features discussed may also be implemented in other forms (e.g., a device or program). The device may be implemented in, for example, appropriate hardware, software, and firmware. These methods may be implemented in, for example, a processor, which generally refers to a processing device, including, for example, a computer, a microprocessor, an integrated circuit, or a programmable logic device. The processor also includes communication equipment, such as, for example, a computer, a mobile phone, a portable / personal digital assistant ("PDA"), and other equipment that facilitates information communication between end users.

[0121] References to "one example" or "an example" or "one implementation" or "an implementation" and other variations thereof mean that a particular feature, structure, characteristic, etc. described in connection with the example is included in at least one example. Thus, the phrases "in one example" or "in an example" or "in one implementation" or "in an implementation" and any other variations appearing in various places throughout this application are not necessarily all referring to the same example.

[0122] Additionally, the present application may involve "determining" various pieces of information. Determining information may include, for example, one or more of estimating information, calculating information, predicting information, or retrieving information from a memory. Obtaining may include receiving, retrieving, constructing, generating, and / or determining.

[0123] Furthermore, the present application may refer to "accessing" various information. Accessing information may include, for example, one or more of receiving information, retrieving information (e.g., from a memory), storing information, moving information, copying information, calculating information, determining information, predicting information, or estimating information.

[0124] Additionally, the present application may refer to "receiving" various information. Like "accessing," receiving is intended to be a broad term. Receiving information may include, for example, one or more of accessing information or retrieving information (e.g., from a memory). Furthermore, "receiving" generally involves, in one way or another, during operations such as, for example, storing information, processing information, sending information, moving information, copying information, erasing information, calculating information, determining information, predicting information, or estimating information.

[0125] It should be understood that, for example, in the case of "A / B," "A and / or B," and "at least one of A and B," the use of any of the following " / ," "and / or," and "at least one of" is intended to encompass selecting only the first listed option (A), or only the second listed option (B), or both options (A and B). As a further example, in the case of "A, B, and / or C" and "at least one of A, B, and C," such phrases are intended to encompass selecting only the first listed option (A), or only the second listed option (B), or only the third listed option (C), or only the first listed option and the second listed option (A and B), or only the first listed option and the third listed option (A and C), or only the second listed option and the third listed option (B and C), or all three options (A and B and C). This can be extended to as many items as listed, as will be apparent to one of ordinary skill in this and related arts.

[0126] Moreover, as used herein, the term "signaling" means (among other things) indicating something to a corresponding decoder. The encoder signal may include, for example, a signal indicating whether the encoder selects an implicit or explicit determination of one or more adjustment values, etc. Thus, in the example, the same parameters are used on both the encoder side and the decoder side. Thus, for example, the encoder may send (explicit signaling) specific parameters to the decoder so that the decoder can use the same specific parameters. On the contrary, if the decoder already has specific parameters and other parameters, signaling may be used without sending (implicit signaling) to simply allow the decoder to know and select specific parameters. Bit savings are achieved in various examples by avoiding sending any actual function. It should be understood that signaling can be implemented in a variety of ways. For example, in various examples, one or more syntax elements, flags, etc. are used to signal information to the corresponding decoder. Although the verb form of the term "signaling" is previously referred to, the term may also be used as a noun "signal" herein.

[0127] It will be apparent to one of ordinary skill in the art that a specific implementation may generate a variety of signals formatted to carry, for example, storable or transmittable information. The information may include, for example, instructions for executing a method or data generated by one of the described specific implementations. For example, a signal may be formatted to carry a bit stream of the example. Such a signal may be formatted as, for example, an electromagnetic wave (e.g., using a radio frequency portion of a spectrum) or a baseband signal. Formatting may include, for example, encoding a data stream and modulating a carrier using the encoded data stream. The information carried by the signal may be, for example, analog or digital information. As is well known, a signal may be sent over a variety of different wired or wireless links. The signal may be stored on, or accessed or received from, a processor-readable medium.

[0128] Many examples are described herein. The features of the examples may be provided individually or in any combination across various claim categories and types. In addition, the examples may include one or more of the features, devices or aspects described herein individually or in any combination across various claim categories and types. For example, the features described herein may include a bitstream or signal of information generated as described herein to be implemented. This information may allow a decoder to decode a bitstream, an encoder, a bitstream and / or a decoder according to any of the embodiments described in the embodiments. For example, the features described herein may be implemented by creating and / or sending and / or receiving and / or decoding a bitstream or a signal. For example, the features described herein may be implemented by a method, a process, a device, a medium storing instructions, a medium storing data or a signal. For example, the features described herein may be implemented by a TV, a set-top box, a mobile phone, a tablet computer or other electronic devices that perform decoding. A TV, a set-top box, a mobile phone, a tablet computer or other electronic devices may display (e.g., using a monitor, a screen or other type of display) the resulting image (e.g., an image reconstructed from the residual of a video bitstream). A TV, a set-top box, a mobile phone, a tablet computer or other electronic devices may receive a signal including an encoded image and perform decoding.

[0129] Features described herein may be associated with adjusting a cross component linear model (CCLM) and a multi-mode linear model (MMLM). CCLM and MMLM may be chroma prediction modes with a prediction signal generated by a luma component. CCLM and MMLM may be modified by a scale factor and a slope. The slope value may be modified. For example, the slope may be adjusted based on reconstructed pixels (e.g., reconstructed luma and chroma samples) around the current block.

[0130] Cross-component prediction may be performed. For example, template-based CCLM / MMLM (e.g., in the context of video coding) with (e.g., implicit) slope adjustment may be performed. CCLM and MMLM may be chroma prediction modes. Predictions of chroma samples may be generated from luma samples in a coded block. The prediction model may include a bias factor and a slope. For example, the slope value may be adjusted based on reconstructed pixels surrounding the current chroma block.

[0131] In cross-component prediction, cross-component redundancy can be reduced, for example, by modeling the relationship between luma and chroma components. For example, in CCLM, linear model parameters can be derived from reconstructed luma and chroma samples around the current block. For example, signaling can be reduced or avoided, and the same model derivation process (e.g., CCLM) can be repeated at the decoder side.

[0132] For example, when multiple linear models are utilized, CCLM can be extended to MMLM. In an example, MMLM can include multiple CCLMs (eg, two CCLMs). Parameters for multiple prediction models can be derived at the encoder side and the decoder side.

[0133] In an example, the slope may be adjusted by testing different values ​​at the encoder side.The slope adjustment value may be signaled to the decoder (eg, may be signaled by the encoder to the decoder).

[0134] For example, the slope adjustment may be obtained by deriving a slope adjustment value at the encoder side and the decoder side. In an example, multiple slopes may be determined for multiple adjustment models. The prediction model may be adjusted based on the selected adjustment model. Signaling overhead may be reduced (e.g., removed) and / or the bit rate may be modified (e.g., by deriving a slope adjustment value at the encoder side and the decoder side).

[0135] The CCLM prediction mode may be used in video coding. For example, chrominance samples may be predicted based on reconstructed luma samples (eg, for the same coding block) using a linear model. For example, the linear model may be implemented according to equation (1):

[0136] pred C (i,j)=α·rec L ′(i,j)+β (1)

[0137] As shown in the example in equation (1), pred C (i, j) may represent the predicted chroma samples in the CU. As shown in the example of equation (1), rec L ′(i,j) may represent the downsampled reconstructed luma samples of the CU.

[0138] The CCLM parameters (e.g., α and β) may be derived, for example, based on (e.g., up to four) neighboring chroma samples and corresponding downsampled luma samples. The current chroma block size may be W×H. In an example, W'' and H' may be set according to the following logic:

[0139] For example, if / when LM mode is applied, W'=W, H'=H;

[0140] For example, if / when LM-A mode is applied, W'=W+H; and / or

[0141] For example, if / when the LM-L mode is applied, H'=H+W.

[0142] The adjacent position above may be represented as S[0,-1]...S[W'-1,-1], and the adjacent position to the left may be represented as S[-1,0]...S[-1,H'-1]. Four samples may be selected (eg, according to example logic) as follows:

[0143] For example, if / when LM mode is applied and both above and left neighboring samples are available,

[0144] S[W' / 4,-1], S[3*W' / 4,-1], S[-1,H' / 4], S[-1,3*H' / 4];

[0145] For example, if / when LM-A mode is applied or only upper neighbor samples are available,

[0146] S[W' / 8,-1], S[3*W' / 8,-1], S[5*W' / 8,-1], S[7*W' / 8,-1]; and / or

[0147] For example, if / when LM-L mode is applied or only left neighbor samples are available,

[0148] S[-1,H' / 8], S[-1,3*H' / 8], S[-1,5*H' / 8], S[-1,7*H' / 8].

[0149] In an example, four adjacent luma samples at the selected position may be downsampled and compared four times to find 0 A and x 1 A The two larger values ​​of and denoted as x 0 B and x 1 B The corresponding chrominance sample value can be expressed as y 0 A ,y 1 A ,y 0 B and 1 B In this example, x A 、x B ,y A and B It can be derived, for example, from equations (2a-2d):

[0150] X a =(x 0 A +x 1 A +1)>>1(2a)

[0151] X b =(x 0 B +x 1 B +1)>>1(2b)

[0152] Y a =(y 0 A +y 1 A +1)>>1(2c)

[0153] Y b =(y 0 B +y 1 B +1)>>1(2d)

[0154] The linear model parameters α and β can be determined, for example, according to equations (3) and (4):

[0155]

[0156] β=Y b -α·X b (4)

[0157] Figure 5 An example of the positions of left and upper samples and samples of the current block involved in the CCLM mode is illustrated. Figure 5 An example of the location of samples used to derive the linear model parameters α and β is shown.

[0158] The division operation used to calculate the parameter α (e.g., as shown in equation (3)) can be implemented, for example, using a lookup table. The difference between the maximum and minimum values ​​(e.g., the diff value) and / or the parameter α can be represented in exponential notation, for example to reduce the amount of memory used to store the table. In an example, diff can be approximated as a 4-bit significand and an exponent. For example, according to equation (5), the table for 1 / diff can be reduced to 16 elements for 16 values ​​of the significand:

[0159] DivTable [ ] = { 0, 7, 6, 5, 5, 4, 4, 3, 3, 2, 2, 1, 1, 1, 1, 0}(5)

[0160] The difference diff (eg, as shown in the example) may be approximated.

[0161] The upper template and the left template may be used in combination to calculate the linear model coefficients.The upper template and the left template may be used (eg, alternatively, independently) in one or more (eg, other) LM modes (eg, LM_T and LM_L modes).

[0162] In an example (e.g., in LM_T mode), the upper template (e.g., only the upper template) can be used to calculate the linear model coefficients. The upper template can be expanded to, for example, (W+H) samples to obtain more samples. In an example (e.g., in LM_L mode), the left template (e.g., only the left template) can be used to calculate the linear model coefficients. For example, the left template can be expanded to (H+W) samples to obtain more samples. In an example (e.g., in LM_LT mode), the left and upper templates can be used in combination to calculate the linear model coefficients.

[0163] The chroma sample positions may be matched for a video sequence (e.g., for a 4:2:0 video sequence). Multiple (e.g., two) types of downsampling filters may be applied to luma samples to achieve a downsampling ratio in the horizontal and / or vertical directions (e.g., a 2 to 1 downsampling ratio). The selection of the downsampling filter may be specified, for example, by a sequence parameter set (SPS) level flag. For example, according to equations (6) and (7), the downsampling filters may correspond to "type-0" and "type-2" content, respectively:

[0164]

[0165] For example, if / when the upper reference line is at a CTU boundary, a luma line (eg, a general line buffer in intra prediction) may be used to determine or generate downsampled luma samples.

[0166] The prediction model parameters are calculated as part of the decoding process and the signaling of these parameters may be bypassed or skipped.

[0167] In an example (e.g., for chroma intra-mode coding), multiple intra-modes (e.g., 8 total intra-modes) may be allowed (e.g., and utilized) for chroma intra-mode coding. The intra-modes may include one or more (e.g., five legacy) intra-modes and one or more (e.g., three) cross-component linear model modes (e.g., CCLM, LM_A, and LM_L).

[0168] Table 1 shows an example of chroma mode signaling and derivation process. Chroma mode coding may depend on the intra prediction mode of the corresponding luma block. For example, if / when separate block partition structures for luma and chroma components are enabled in an I slice, a chroma block may correspond to multiple luma blocks. In an example (e.g., for chroma DM mode), the intra prediction mode of the corresponding luma block covering the center position of the current chroma block may be inherited.

[0169] Table 1 - Example of deriving chrominance prediction modes from luma modes when CCLM is enabled

[0170]

[0171] For example, regardless of the value of sps_cclm_enabled_flag, a binarization table (eg, a single binarization table) may be used.

[0172] MMLM can be used to predict the decoding block. CCLM can be implemented using, for example, one or more (e.g., three) multi-mode LM (MMLM) modes. In (e.g., each) MMLM mode, the reconstructed adjacent samples can be classified into multiple (e.g., two) classes, for example, using a threshold. In an example, the threshold can be the average value of the reconstructed adjacent brightness samples. For example, a least mean square (LMS) method is used to derive a linear model for (e.g., each) class. The LMS method can be used to derive a linear model for the CCLM mode.

[0173] The slope of the prediction model can be adjusted. CCLM can use a prediction model with one or more (e.g., two) parameters to map luma values ​​to chroma values. For example, according to equation (8), the slope parameter "a" and the bias parameter "b" can define the mapping:

[0174] chromaVal = a * lumaVal + b (8)

[0175] For example, according to equation (9), an adjustment "u" to the slope parameter may be signaled to update the model:

[0176] chromaVal = a' * lumaVal + b' (9)

[0177] For example, according to equations (10a-10b), the updated slope parameter can be determined:

[0178] a'=a+u(10a)

[0179] b'=bu*y r (10b)

[0180] For example, based on this selection, the mapping function may be centered around a value y having a brightness value r The average of the reference brightness samples used in model creation can be used as the y r , for example to provide a (eg, meaningful) modification to the model. Figure 6 illustrates an example of a process.

[0181] Fig. 6A and Figure 6B An example effect of the slope adjustment parameter "u" is illustrated. Fig. 6A A model of the CCLM without updated slope parameters is shown. Figure 6B The model of the CCLM with updated slope parameters is shown.

[0182] In an example, the slope adjustment parameter may be an integer between -4 and 4, inclusive. The slope adjustment parameter may be signaled in the video data (e.g., a bitstream). The unit of the slope adjustment parameter may be, for example, 1 / 8 of a chroma sample value per luma sample value (e.g., for 10-bit content).

[0183] In an example, adjustments are available for CCLM models that use reference samples above and to the left of a block (e.g., "LM_CHROMA_IDX" and "MMLM_CHROMA_IDX"), but not for "one-sided" mode. For example, the availability of adjustments for CCLM models may be based on a tradeoff between coding efficiency and complexity.

[0184] For a coding block using a multi-mode CCLM model, multiple prediction models may be adjusted. For example, a slope adjustment may be applied to each prediction model associated with the multi-mode CCLM model. One or more slope updates may be signaled for a chroma block.

[0185] In an example, the encoder may determine the slope adjustment. For example, the encoder may perform a sum of absolute transform differences (SATD)-based search for Cr to find the best value for the slope update. The encoder may perform a similar SATD-based search for Cb. For example, if there is a non-zero slope adjustment parameter for Cr or Cb, the combined slope adjustment pair (e.g., SATD-based update for Cr, SATD-based update for Cb) may be included in a list of rate-distortion (RD) checks for a transform unit (TU).

[0186] Figure 7 An example of deriving an adjustment model based on reconstructed luma and chroma samples is illustrated. For example, the adjustment value may be calculated using reconstructed luma and chroma samples adjacent to the current block. The adjustment value may be calculated at the decoder side and the encoder side. Figure 7 As shown in the example in, the slope adjustment value can be derived at the decoder side (e.g., and encoder side) using, for example, a template consisting of available reconstructed luma and chroma samples. Luma and chroma samples may include the same samples used to derive CCLM / MMLM parameters (e.g., CCLM and MMLM parameters may be based on the obtained reconstructed luma and chroma samples). The adjustment value can be derived using CCLM or MMLM. For example, the adjustment value can be calculated by testing possible values ​​about the reconstructed samples. The selected adjustment value can be a possible value that minimizes the distance.

[0187] The slope adjustment may be implicit (e.g., explicit signaling of the slope adjustment may be bypassed). The adjustment value (u) may be derived at the decoder side. For example, rather than signaling the adjustment value (u) from the encoder to the decoder, the value may be inferred at the decoder side.

[0188] In an example, chroma samples available to the decoder may be used to derive the adjustment value (u).

[0189] Figure 8 An example of using chroma samples to derive adjustment values ​​is illustrated.

[0190] The template may include W*N samples above the current block and H*M samples to the left of the current block. Parameters W and H may represent width and height, respectively. Different values ​​of the adjustment value "u" may be tested at the encoder and decoder. CCLM / MMLM may be performed on the template at the encoder and decoder. For example, a cross-component prediction model may be applied with potential adjustment values ​​to predict chroma samples in the template using their corresponding concatenated reconstructed luminance samples. The chroma samples predicted using various adjustment values ​​may be compared with the actual reconstructed chroma samples in the template. The adjustment value that results in the most accurate prediction may be selected as the adjustment value for the model of the current block. In an example, an operation may be performed for an adjustment value "u" from -4 to 4. The selected (e.g., optimal) value of u may be a value that provides the most accurate (e.g., optimal) prediction of the template. The value of u may be determined without signaling. In an example, N and M may be equal. In an example, N and M may depend on the size of the current block.

[0191] For example, if / when the upper sample (e.g., only the upper sample) is considered for CCLM / MMLM (LM_A), the upper template (e.g., only the upper template) can be considered for deriving the u value. For example, if / when the left sample (e.g., only the left sample) is considered for CCLM / MMLM (LM_A), the left template (e.g., only the left template) can be considered for deriving the u value. For example, if / when the upper sample and the left sample are considered for CCLM / MMLM (LM_A), the upper template and the left template can be considered to derive the u value.

[0192] The adjustment determination indication may indicate implicit or explicit signaling of the adjustment value. The encoder may determine whether the adjustment value is to be explicitly signaled in the bitstream or to be derived / inferred by the decoder, and indicate the determination via an implicit or explicit adjustment flag. A flag may be signaled to indicate (e.g., to the decoder) whether the adjustment value (u) is signaled to the decoder or inferred by the decoder. For example, an implicit slope adjustment may be indicated by a flag equal to zero, and an explicit slope adjustment may be indicated by a flag not equal to zero (e.g., a flag equal to one).

[0193] The number of adjustment values ​​may vary. In an example, there may be eight (8) adjustment values ​​(e.g., -4 -3 -2 -1 12 3 4). Eight adjustment values ​​may provide a reasonable tradeoff between good prediction and signaling overhead. For implicit slope adjustment without signaling, the restriction on the number of adjustment values ​​may be alleviated. In an example, non-integer values ​​between -4 and 4 may be used as adjustment values. In an example, a range (e.g., a wide range) of integer and / or non-integer adjustment values ​​(e.g., -10 to 10) may be used. The wide (e.g., high) range of numbers to be tested may correspond to encoder and decoder computational complexity (e.g., additional computational complexity). In an example, a reasonable tradeoff for selecting a range of adjustment values ​​may be achieved.

[0194] Different adjustment models can be used to adjust the prediction model used for the coding block. The slope adjustment of the prediction model can be based on modifying the slope (y r ,like Figure 6B ). The slope adjustment may be based on pivoting the prediction model around extreme points, such as maximum and / or minimum brightness values. FIG. 9A to FIG. 9B An example of slope modification is shown. In some examples, the slope adjustment can be based on modifying the slope around a pivot point indicated in the video data.

[0195] FIG. 9A to FIG. 9B An example of slope modification around an extreme value is illustrated. Fig.9A An example of slope modification around a minimum luminance value is shown. Fig. 9B An example of a slope modification around a maximum brightness value is shown. For example, an example adjustment model may include a slope modification around a limit brightness value.

[0196] Slope modification around limiting luma values ​​may provide flexibility in selecting a suitable slope for a particular coding block.Slope modification around limiting luma values ​​may not require additional (eg, further) signaling.

[0197] For example, the prediction model for the coding block may include a linear prediction model. For example, the linear function may be modified by pivoting around a minimum value and / or around a maximum value. For example, the minimum value of the linear prediction model may be a pivot point for slope modification. The maximum value of the linear prediction model may be a pivot point for slope modification.

[0198] A plurality of adjustment models may be obtained for adjusting the prediction model of the coding block. For example, in an example adjustment model, the slope of the prediction model may be adjusted around a pivot point, and the pivot point may be a minimum value of the prediction model. In an example adjustment model, the slope of the prediction model may be adjusted around a pivot point, and the pivot point may be a maximum value of the prediction model. In an example adjustment model, the slope of the prediction model may be adjusted around a pivot point, and the pivot point may be a midpoint of the prediction model.

[0199] In an example of modification around a minimum value, assuming that Ymin is the minimum value of the collocated luma samples, the a and b values ​​may be modified, for example, according to equations (11a) and (11b):

[0200] a=a*u(11a)

[0201] b=b–u*Ymin(11b)

[0202] In an example of modification around a maximum, assuming that Ymax is the minimum value of the collocated luma samples, the a and b values ​​may be modified, for example, according to equations (12a) and (12b):

[0203] a=a*u(12a)

[0204] b=b–u*Ymax(12a)

[0205] In the example adjustment model, the offset value (e.g., constant value b) may be modified with or without modifying the slope. In an example, the prediction model may be adjusted based on the selected adjustment model by adjusting the offset of the prediction model and maintaining (e.g., not adjusting) the slope (e.g., of the prediction model). Fig.10 An example of changing a constant value (eg, changing only the constant value) is shown.

[0206] Fig.10 An example of modifying the linear slope value b is illustrated. For example, the modification of the slope value b can be achieved according to equation (13):

[0207] b = b + u (13)

[0208] A plurality of parameters (e.g., slope and offset) of the prediction model may be derived (e.g., by a video decoder) based on reconstructed samples adjacent to the coding block. The decoder may adjust the prediction model based on the selected adjustment model. The selected adjustment model may be configured to adjust the slope and / or offset of the prediction model.

[0209] The example adjustment models described herein may be implemented individually or in any combination. For example, different u values ​​may be tested on the encoder side and the decoder side, the slope may be changed around the midpoint and extreme points on the encoder side and the decoder side, and / or the constant value may be changed on the encoder side and the decoder side. For example, a (e.g., optimal) model may be selected based on the measured distance with or without signaling. Although the examples provided herein relate to predicting chrominance samples based on collocated luminance samples, those skilled in the art will appreciate that the examples may be applicable to predicting luminance samples based on collocated chrominance samples.

[0210] In an example, an encoder may select an adjustment model for adjusting a prediction model for a coding block from a plurality of adjustment models. For example, the encoder may perform a search based on the sum of absolute transform differences (SATD) for Cr to find the best adjustment model. SATD distances (e.g., distances associated with each adjustment model) may be calculated for available adjustment models by calculating the SATD between sample (e.g., chrominance and luminance) values ​​of the current block and the predicted sample values. The calculated SATD distances for different adjustment models may be used to select an adjustment model suitable for the current block. The prediction model may be adjusted based on the selected adjustment model.

[0211] A plurality of adjustment models may be obtained (e.g., by a video encoder) for use in adjusting a prediction model. A first difference (e.g., SATD) for a first adjustment model may be calculated (e.g., by a video encoder) between sample values ​​of a coding block and predicted sample values ​​predicted based on a first adjustment model. A second difference for a second adjustment model may be calculated (e.g., by a video encoder) between sample values ​​of a coding block and predicted sample values ​​predicted based on a second adjustment model. An adjustment model may be selected based on comparing the first difference and the second difference. The selected adjustment model may be indicated in the video data.

[0212] The selected adjustment model may be obtained based on an indication in the video data, as shown in the following table. For example, in Table 2, the selected adjustment model may be obtained based on an adjustment model indication (e.g., in the video data). For example, the adjustment model indication may be or may include the AdjustModel indication shown in Table 2. Based on the adjustment model indication, one or more pivot points of the selected adjustment model may be determined. For example, the pivot points may be signaled by Pivotpoint_M1 and Pivotpoint_M2.

[0213] Table 2: Examples for signaling pivot points, including syntax for adjusting models and pivot points .

[0214]

[0215] As shown in Table 2, the selected adjustment model can be obtained based on an adjustment model indication (e.g., in the video data), which indicates a first pivot point (e.g., Pivotpoint_M1) of the selected adjustment model and a second pivot point (e.g., Pivotpoint_M2) of the adjustment model. For example, the prediction model for the block may include multiple prediction models. When the MMLM is used for the decoding block, multiple linear prediction models are used to predict the decoding block. Adjusting the prediction model for the decoding block can be based on multiple pivot points, for example, the first pivot point of the selected adjustment model and the second pivot point of the selected adjustment model. Based on the selected adjustment model, it can be determined that the first prediction model for the decoding block pivots around the first pivot point of the first prediction model, and the second prediction model for the decoding block pivots around the second pivot point of the selected adjustment model.

[0216] The first pivot point may be based on a first adjustment model, and the second pivot point may be based on a second adjustment model. The first pivot point may be used to adjust a first prediction model among a plurality of prediction models. The second pivot point may be used to adjust a second prediction model among a plurality of linear prediction models. The video decoder may adjust a slope of the first linear prediction model via the determined first pivot point, and adjust a slope of the second linear prediction model via the determined second pivot point.

[0217] For example, in Table 3, the selected adjustment model may be obtained based on an adjustment model indication (e.g., in the video data) that indicates a pivot point of the selected adjustment model. For example, based on the indication isMidPoint, it may be determined whether the pivot point of the selected adjustment model is the midpoint of the prediction model. Based on the indication PivotPoint, it may be determined whether to use the start point or the end point of the prediction model as the pivot point. For example, PivotPoint=1 may indicate the start of the prediction model as the pivot point, and PivotPoint=2 may indicate the end of the prediction model as the pivot point.

[0218] Table 3: Examples for determining starting and ending points (e.g., for pivoting a prediction model), including midpoints and pivots Syntax of a dot .

[0219]

[0220] For example, in Table 4, the selected adjustment model may be obtained based on an adjustment model indication (e.g., in the video data) indicating a pivot point. As shown in Table 4, the pivot point may be selected from a plurality of predefined points (such as 2N+1 potential pivot points). The predefined points may include a midpoint and N points on each side of the midpoint (e.g., N predefined potential pivot points above the midpoint and a predefined potential pivot point below the midpoint). The adjustment model indication may be configured to indicate which predefined points the prediction model will be adjusted around.

[0221] Table 4: Example for determining 2N+1 pivot points (eg, for pivoting a prediction model), including midpoints and pivots Syntax of Pivot Points .

[0222]

[0223]

[0224] As shown in Table 5, Log2(N) bits may be processed (eg, encoded and / or decoded). For example, 4 points (eg, pivot points) may be processed (eg, encoded and / or decoded) using 2 bits.

[0225] Table 5: Example of 2N+1 pivot points (eg, for pivoting a prediction model), including syntax of the pivot points .

[0226]

[0227] Although the examples provided herein may assume that media content is streamed to a display device, there is no particular restriction on the type of display device that may benefit from the example techniques described herein. For example, a display device may be a television, a projector, a mobile phone, a tablet computer, etc. In addition, the example techniques described herein may not only be applicable to streaming use cases, but may also be applicable to teleconference settings. In addition, decoders and displays as described herein may be separate devices or may be parts of the same device. For example, a set-top box may decode an incoming video stream and (for example, subsequently) provide the decoded stream to a display device (for example, via HDMI), and information about viewing conditions such as viewing distance may be transmitted to a set-top box (for example, via HDMI) from a display device.

[0228] Although features and elements are described above in specific combinations, it will be understood by those of ordinary skill in the art that each feature or element may be used alone or in any combination with other features and elements. In addition, the methods described herein may be implemented in a computer program, software, or firmware incorporated in a computer-readable medium for execution by a computer or processor. Examples of computer-readable media include electronic signals (sent via a wired or wireless connection) and computer-readable storage media. Examples of computer-readable storage media include, but are not limited to, read-only memory (ROM), random access memory (RAM), registers, cache memory, semiconductor memory devices, magnetic media (such as built-in hard disks and removable disks), magneto-optical media, and optical media (such as CD-ROM disks and digital versatile disks (DVDs)). A processor associated with software may be used to implement a radio frequency transceiver for a WTRU, UE, terminal, base station, RNC, or any host computer.

Claims

1. A video decoding device, the video decoding device comprising: A processor, the processor being configured to: Obtaining a prediction model for predicting a decoding block; Selecting an adjustment model from a plurality of adjustment models for adjusting the prediction model; adjusting the prediction model based on the selected adjustment model; and The coding block is decoded based on the adjusted prediction model.

2. The apparatus of claim 1, wherein the processor is further configured to: Deriving a slope and an offset of the prediction model based on reconstructed samples adjacent to the coding block, wherein adjusting the prediction model based on the selected adjustment model comprises: At least one of the slope or the offset of the prediction model is adjusted based on the selected adjustment model.

3. The apparatus of claim 1 , wherein the prediction model comprises a cross-component prediction model, and the processor is further configured to: Multiple parameters of the cross-component prediction model are determined based on neighboring chroma samples and luma samples of the decoding block, wherein a first adjustment model and a second adjustment model of the multiple adjustment models are configured to adjust the multiple parameters of the cross-component prediction model in different ways.

4. The apparatus of claim 1 , wherein a first adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a minimum value, a second adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a midpoint value, and a third adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a maximum value.

5. The apparatus of claim 1 , wherein the processor is further configured to: The prediction model is adjusted based on the selected adjustment model by adjusting the offset of the prediction model and maintaining the same slope of the prediction model.

6. The apparatus of claim 1 , wherein the selected adjustment model is obtained based on an adjustment model indication in the video data, and wherein the processor is further configured to: Based on the adjustment model indication, determining a pivot point of the selected adjustment model, wherein adjusting the prediction model based on the selected adjustment model comprises: The slope of the prediction model is adjusted via the determined pivot point.

7. The apparatus of claim 1 , wherein the selected adjustment model is obtained based on an adjustment model indication in the video data, and wherein the processor is further configured to: determining an offset adjustment for the selected adjustment model based on the adjustment model indication; and The bias of the prediction model is adjusted based on the determined bias adjustment of the selected adjustment model.

8. The apparatus according to claim 1, wherein the prediction model used to predict the decoding block comprises a plurality of linear prediction models, and the processor is further configured to: determining a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models based on the selected adjustment model; determining a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models based on the selected adjustment model; adjusting a slope of the first linear prediction model via the determined first pivot point; as well as The slope of the second linear prediction model is adjusted via the determined second pivot point.

9. The apparatus according to claim 1, wherein the prediction model used to predict the decoding block comprises a plurality of linear prediction models, and the processor is further configured to: determining a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models based on the first adjustment model; determining a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models based on the second adjustment model; adjusting a slope of the first linear prediction model via the determined first pivot point; as well as The slope of the second linear prediction model is adjusted via the determined second pivot point.

10. A video encoding device, the video encoding device comprising: A processor, the processor being configured to: Obtaining a prediction model for predicting a decoding block; selecting an adjustment model from a plurality of adjustment models for adjusting the prediction model; adjusting the prediction model based on the selected adjustment model; and The coding block is encoded based on the adjusted prediction model.

11. The apparatus of claim 10, wherein the processor is further configured to: obtaining the plurality of adjustment models configured to adjust the prediction model; For a first adjustment model among the plurality of adjustment models, calculating a first difference between a sample value of the decoding block and a predicted sample value predicted based on the first adjustment model; and For a second adjustment model among the plurality of adjustment models, a second difference between the sample value of the decoding block and a predicted sample value predicted based on the second adjustment model is calculated, wherein the adjustment model is selected based on at least comparing the first difference with the second difference.

12. The apparatus of claim 10, wherein the prediction model comprises a cross-component prediction model, and the processor is further configured to: Multiple parameters of the cross-component prediction model are determined based on neighboring chroma samples and luma samples of the decoding block, wherein a first adjustment model and a second adjustment model of the multiple adjustment models are configured to adjust the multiple parameters of the cross-component prediction model in different ways.

13. The apparatus of claim 10, wherein a first adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a minimum value, a second adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a midpoint value, and a third adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a maximum value.

14. The apparatus of claim 10, wherein the processor is further configured to: The prediction model is adjusted based on the selected adjustment model by adjusting the offset of the prediction model and maintaining the same slope of the prediction model.

15. The device of claim 10, wherein the processor is further configured to: Determining a pivot point of the selected adjustment model, wherein adjusting the prediction model based on the selected adjustment model comprises: adjusting the slope of the prediction model via the determined pivot point; as well as An adjustment model indicator is included in the video data, the adjustment model indicator indicating the selected adjustment model and the determined pivot point of the selected adjustment model.

16. The device of claim 10, wherein the processor is further configured to: determining an offset adjustment for the selected adjustment model; adjusting the bias of the prediction model based on the determined bias adjustment of the selected adjustment model; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the selected adjustment model and the determined offset adjustment of the selected adjustment model.

17. The apparatus according to claim 10, wherein the prediction model used to predict the decoding block comprises a plurality of linear prediction models, and the processor is further configured to: determining a first pivot point for adjusting a first linear prediction model among the plurality of linear prediction models; determining a second pivot point for adjusting a second linear prediction model among the plurality of linear prediction models; adjusting a slope of the first linear prediction model via the determined first pivot point; adjusting the slope of the second linear prediction model via the determined second pivot point; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the first pivot point and the second pivot point associated with the selected adjustment model.

18. The apparatus according to claim 10, wherein the prediction model used to predict the decoding block comprises a plurality of linear prediction models, and the processor is further configured to: determining a first adjustment model associated with a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models; determining a second adjustment model associated with a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models; adjusting a slope of the first linear prediction model via the determined first pivot point; adjusting the slope of the second linear prediction model via the determined second pivot point; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the first adjustment model and the second adjustment model.

19. The device of claims 1 to 18, further comprising a memory operatively connected to the processor.

20. A method for a video decoder, the method comprising: Obtaining a prediction model for predicting a decoding block; Selecting an adjustment model from a plurality of adjustment models for adjusting the prediction model; adjusting the prediction model based on the selected adjustment model; and The coding block is decoded based on the adjusted prediction model.

21. The method according to claim 20, wherein the method further comprises: A slope and an offset of the prediction model are derived based on reconstructed samples adjacent to the coding block, wherein adjusting the prediction model based on the selected adjustment model comprises adjusting at least one of the slope or the offset of the prediction model based on the selected adjustment model.

22. The method of claim 20, wherein the prediction model comprises a cross-component prediction model, and the method further comprises: Multiple parameters of the cross-component prediction model are determined based on neighboring chroma samples and luma samples of the decoding block, wherein a first adjustment model and a second adjustment model of the multiple adjustment models are configured to adjust the multiple parameters of the cross-component prediction model in different ways.

23. The method of claim 20, wherein a first adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a minimum value, a second adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a midpoint value, and a third adjustment model among the plurality of adjustment models is configured to adjust the slope of the prediction model around a maximum value.

24. The method according to claim 20, wherein the method further comprises: The prediction model is adjusted based on the selected adjustment model by adjusting the offset of the prediction model and maintaining the same slope of the prediction model.

25. The method of claim 20, wherein the selected adjustment model is obtained based on an adjustment model indication in the video data, and wherein the method further comprises: Based on the adjustment model indication, a pivot point of the selected adjustment model is determined, wherein adjusting the prediction model based on the selected adjustment model includes adjusting a slope of the prediction model via the determined pivot point.

26. The method of claim 20, wherein the selected adjustment model is obtained based on an adjustment model indication in the video data, and wherein the method further comprises: determining an offset adjustment for the selected adjustment model based on the adjustment model indication; as well as The bias of the prediction model is adjusted based on the determined bias adjustment of the selected adjustment model.

27. The method of claim 20, wherein the prediction model used to predict the coding block comprises a plurality of linear prediction models, and the method further comprises: determining a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models based on the selected adjustment model; determining a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models based on the selected adjustment model; adjusting a slope of the first linear prediction model via the determined first pivot point; as well as The slope of the second linear prediction model is adjusted via the determined second pivot point.

28. The method of claim 20, wherein the prediction model used to predict the coding block comprises a plurality of linear prediction models, and the method further comprises: determining a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models based on the first adjustment model; determining a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models based on the second adjustment model; adjusting a slope of the first linear prediction model via the determined first pivot point; as well as The slope of the second linear prediction model is adjusted via the determined second pivot point.

29. A method for a video encoder, the method comprising: Obtaining a prediction model for predicting a decoding block; Selecting an adjustment model from a plurality of adjustment models for adjusting the prediction model; adjusting the prediction model based on the selected adjustment model; and The coding block is encoded based on the adjusted prediction model.

30. The method of claim 29, wherein the method further comprises: obtaining the plurality of adjustment models configured to adjust the prediction model; For a first adjustment model among the plurality of adjustment models, calculating a first difference between a sample value of the decoding block and a predicted sample value predicted based on the first adjustment model; as well as For a second adjustment model among the plurality of adjustment models, a second difference between the sample value of the decoding block and a predicted sample value predicted based on the second adjustment model is calculated, wherein the adjustment model is selected based on at least comparing the first difference with the second difference.

31. The method of claim 29, wherein the prediction model comprises a cross-component prediction model, and the method further comprises: Multiple parameters of the cross-component prediction model are determined based on neighboring chroma samples and luma samples of the decoding block, wherein a first adjustment model and a second adjustment model of the multiple adjustment models are configured to adjust the multiple parameters of the cross-component prediction model in different ways.

32. A method according to claim 29, wherein a first adjustment model among the multiple adjustment models is configured to adjust the slope of the prediction model around a minimum value, a second adjustment model among the multiple adjustment models is configured to adjust the slope of the prediction model around a midpoint value, and a third adjustment model among the multiple adjustment models is configured to adjust the slope of the prediction model around a maximum value.

33. The method of claim 29, wherein the method further comprises: The prediction model is adjusted based on the selected adjustment model by adjusting the offset of the prediction model and maintaining the same slope of the prediction model.

34. The method of claim 29, wherein the method further comprises: determining a pivot point of the selected adjustment model, wherein adjusting the prediction model based on the selected adjustment model comprises: adjusting a slope of the prediction model via the determined pivot point; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the selected adjustment model and the determined pivot point of the selected adjustment model.

35. The method of claim 29, wherein the method further comprises: determining an offset adjustment for the selected adjustment model; adjusting an offset of the prediction model based on the determined offset adjustment of the selected adjustment model; as well as An adjustment model indicator is included in the video data, the adjustment model indicator indicating the selected adjustment model and the determined offset adjustment of the selected adjustment model.

36. The method of claim 29, wherein the prediction model used to predict the coding block comprises a plurality of linear prediction models, and the method further comprises: determining a first pivot point for adjusting a first linear prediction model among the plurality of linear prediction models; determining a second pivot point for adjusting a second linear prediction model among the plurality of linear prediction models; adjusting a slope of the first linear prediction model via the determined first pivot point; adjusting the slope of the second linear prediction model via the determined second pivot point; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the first pivot point and the second pivot point associated with the selected adjustment model.

37. The method of claim 29, wherein the prediction model used to predict the coding block comprises a plurality of linear prediction models, and the method further comprises: determining a first adjustment model associated with a first pivot point for adjusting a first linear prediction model of the plurality of linear prediction models; determining a first adjustment model associated with a second pivot point for adjusting a second linear prediction model of the plurality of linear prediction models; adjusting a slope of the first linear prediction model via the determined first pivot point; adjusting the slope of the second linear prediction model via the determined second pivot point; and An adjustment model indicator is included in the video data, the adjustment model indicator indicating the first adjustment model and the second adjustment model.

38. A computer program product stored on a non-transitory computer readable medium and comprising program code instructions for implementing the steps of the method according to at least one of claims 20 to 37 when executed by a processor.

39. A computer program comprising program code instructions for implementing the steps of the method according to at least one of claims 20 to 37 when executed by a processor.

40. Video data comprising information representative of the decoded blocks encoded according to one of the methods of any one of claims 29 to 37.

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